This invention relates to target tracking and motion analysis and more specifically, for determining a radiating source""s relative track and motion and the quality of the determined relative track from the data received.
Determining certain track and motion parameters, such as location, range, direction or course and speed, of a target or radiating source, from information items received from the target or radiating source, is a general problem of considerable importance to many types of surveillance systems. For example, a determined location, direction and speed can be used to track a target and anticipate its future location. This projected information can then be provided to a second system, via a wired or wireless network, which may, for example, determine a provide an intercept at the projected future target location. As would be understood in the art, a radiating source or target can be a vehicle, for example, a ship, truck or plane, that actively generates or emits a visual, acoustical or electro-magnetic energy signal, or a vehicle that passively reflects visual, acoustical or electromagnetic energy. Such received reflected data, referred to as contact data, is processed as if the vehicle or target radiated the reflected energy itself. For example, acoustical systems, e.g., SONAR, and electro-magnetic systems, e.g., RADAR, are well known in the art for processing received energy signals that are reflected from a vehicle or target. Accordingly, a source, vehicle or target may be an active or a passive radiating source, and is often referred to in the art as an emitter or contact.
Manual, automatic and computer-aided manual methods for determining location, range and/or course and speed of emitters or contacts are well known in the art. The methods of Track Motion Analysis (TMA) are well known in the art and have a number of common operating characteristics. For example, each method requires a set of measured data (e.g., bearings). And each functional adjusts a set of parameters (such as: x/y position or x/y velocity in the Cartesian plane, to make the parameter set agree with the measured. The parameter set that agrees best with the measured data is deemed to be the solution estimate. An example of a manual method is the Manual Adaptive Target Motion Analysis Evaluator (MATE). In this method, the operator defines a set of data points, edits the data points to remove bad, unacceptable or incorrect data, and modifies the solution parameter set to minimize the errors between the measured data point values and the theoretical value. An example of an automatic or computer-aided method is a Maximum Likelihood Estimator (MLE) where, the algorithm automatically defines the data set based upon a set of algorithm control parameters, pre-edits the measured data to remove bad, unacceptable or incorrect data points, and automatically adjusts the parameter set in an algorithmic manner to achieve a solution that best agrees with the measured data set. A second automatic or computer-aided method is a Modified Polar Kalman Statistical Tracker (MPKAST), which starts with a guess or an estimate of a solution, then processes received data points, which are then edited to remove bad, unacceptable or incorrect data points and iteratively improves the initial guess or estimate to arrive at a solution that best agrees with the received or measured data point set.
However, manual methods are labor intensive and time consuming. They often fail to arrive at a solution in a time interval that is commensurate with the need for the solution. Automatic methods, which can arrive at a solution quickly, can produce solutions significantly in error because of a few bad data points. Computer-aided methods provide a compromise between fast solutions and accurate solutions. These methods allow an operator to evaluate the solution in view of the received data and the ability to remove bad data points that would induce errors in the solution. While computer-aided solutions appear a practical means for determining a target or emitter location, course and speed from single sensor received data, such systems begin to overwhelm the operator as the amount of data received increases or the system is expanded to received data from multiple sensors, which must be integrated and evaluated. In such multi-sensor systems, the computer-aided solution is hampered by the workload imposed upon the operator in evaluating multiple sensor information.
Hence, there is a need for a system operable to process information from a number of data sources that can provide a target tracking and motion solution (i.e. bearing, range, course and speed) with an associated merit of solution quality in an integrated manner while reducing the workload of the operator for evaluation
A method for improving the determination of tracking and motion analysis information obtained from a set of received data points is presented. The method comprises selecting a determined number of data points for processing, determining at least one cost matrix for each information item included in the selected data points, quantifying each of the at least one cost analysis matrix with regard to a known form; and providing a presentation for each of the quantified cost matrices. In one embodiment of the invention, the data points can be selected manually, automatically or automatically but retained with manual intervention. In another embodiment of the invention, each of the quantified cost matrices from different information items are presented in a consistent manner.